OJSP Volume 5 | 2024

This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models.

Neural networks have achieved state-of-the-art performance on the task of acoustic Direction-of-Arrival (DOA) estimation using microphone arrays. Neural models can be classified as end-to-end or hybrid, each class showing advantages and disadvantages. This work introduces Neural-SRP, an end-to-end neural network architecture for DOA estimation inspired by the classical Steered Response Power (SRP) method, which overcomes limitations of current neural models.

In this article, we consider using time-of-arrival (TOA) measurements from a single moving receiver to locate a moving target at constant velocity that emits a periodic signal with unknown signal period. First, we give the TOA measurement model and deduce the Cram e´ r-Rao lower bounds (CRLB). Then, we formulate a nonlinear least squares (NLS) problem to estimate the unknown parameters. We use semidefinite programming (SDP) techniques to relax the nonconvex NLS problem.

Synthetically-generated images are getting increasingly popular. Diffusion models have advanced to the stage where even non-experts can generate photo-realistic images from a simple text prompt. They expand creative horizons but also open a Pandora's box of potential disinformation risks. In this context, the present corpus of synthetic image detection techniques, primarily focusing on older generative models like Generative Adversarial Networks, finds itself ill-equipped to deal with this emerging trend.